scholarly journals Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5930
Author(s):  
Tomas Mendoza ◽  
Chia-Hsuan Lee ◽  
Chien-Hua Huang ◽  
Tien-Lung Sun
Keyword(s):  
Random Forest ◽  
Postural Stability ◽  
Inertial Sensor ◽  
Short Form ◽  
Sensor Data ◽  
Permutation Entropy ◽  
Elderly Subjects ◽  
Clinical Test ◽  
Timed Up And Go ◽  
Axial Acceleration

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.

scholarly journals Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors

2020 ◽  
Vol 10 (19) ◽  
pp. 6931
Author(s):  
Chia-Hsuan Lee ◽  
Chi-Han Wu ◽  
Bernard C. Jiang ◽  
Tien-Lung Sun
Keyword(s):  
Domain Knowledge ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Short Form ◽  
Berg Balance Scale ◽  
Multiscale Entropy ◽  
Clinical Test ◽  
Test Results ◽  
Timed Up And Go ◽  
Clinical Tools

The results obtained by medical experts and inertial sensors via clinical tests to determine fall risks are compared. A clinical test is used to perform the whole timed up and go (TUG) test and segment-based TUG (sTUG) tests, considering various cutoff points. In this paper, (a) t-tests are used to verify fall-risk categorization; and (b) a logistic regression with 100 stepwise iterations is used to divide features into training (80%) and testing sets (20%). The features of (a) and (b) are compared, measuring the similarity of each approach’s decisive features to those of the clinical-test results. In (a), the most significant features are the Y and Z axes, regardless of the segmentation, whereas sTUG outperforms TUG in (b). Comparing the results of (a) and (b) based on the overall TUG test, the Z axis multiscale entropy (MSE) features show significance regardless of the approach: expert opinion or logistic prediction. Among various clinical test combinations, the only commonalities between (a) and (b) are the Y-axis MSE features when walking. Thus, machine learning should be based on both expert domain knowledge and a preliminary analysis with objective screening. Finally, the clinical test results are compared with the inertial sensor results, prompting the proposal for multi-oriented data analysis to objectively verify the sensor results.

scholarly journals Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4537 ◽  
Author(s):  
O’Brien ◽  
Hidalgo-Araya ◽  
Mummidisetty ◽  
Vallery ◽  
Ghaffari ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Inertial Sensor ◽  
Intraclass Correlation ◽  
Sensor Data ◽  
Gait Velocity ◽  
Clinical Test ◽  
Time Step ◽  
Timed Up And Go ◽  
High Resolution Data ◽  
Increased Risk

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features during routine clinical outcome tests, and evaluated changes in sensor-derived measurements with age, sex, height, and weight. Age-ranged, healthy individuals (N = 49, 20–70 years) wore a lower back IMU during the 10 m walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). Spatiotemporal gait parameters computed from the sensor data were validated against gold standard measures, demonstrating excellent agreement for stance time, step time, gait velocity, and step count (intraclass correlation (ICC) > 0.90). There was good agreement for swing time (ICC = 0.78) and moderate agreement for step length (ICC = 0.68). A total of 184 features were calculated from the acceleration and angular velocity signals across these tests, 36 of which had significant correlations with age. This approach was also demonstrated for an individual with stroke, providing higher resolution information about balance, gait, and mobility than the clinical test scores alone. Leveraging mobility data from wireless, wearable sensors can help clinicians and patients more objectively pinpoint impairments, track progression, and set personalized goals during and after rehabilitation.

A Novel Approach for Fall Risk Prediction Using the Inertial Sensor Data From the Timed-Up-and-Go Test in a Community Setting

2020 ◽  
Vol 20 (16) ◽  
pp. 9339-9350 ◽  
Author(s):  
Yu-Cheng Hsu ◽  
Yang Zhao ◽  
Kuang-Hui Huang ◽  
Ya-Ting Wu ◽  
Javier Cabrera ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Fall Risk ◽  
Inertial Sensor ◽  
Community Setting ◽  
Sensor Data ◽  
Timed Up And Go ◽  
Novel Approach

scholarly journals Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0155984 ◽  
Author(s):  
Danique Vervoort ◽  
Nicolas Vuillerme ◽  
Nienke Kosse ◽  
Tibor Hortobágyi ◽  
Claudine J. C. Lamoth
Keyword(s):  
Multivariate Analyses ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Aging Effects ◽  
Timed Up And Go

scholarly journals Correlation between clinical tests for gait and stability using biomechanical variables in the gait of institutionalized elderly subjects

2021 ◽  
Vol 13 (1) ◽  
pp. 49-55
Author(s):  
Felipe H. Palma ◽  
Sebastián Cisternas Rodríguez ◽  
Francisco Vargas Buton ◽  
Marcela Olmos Nieva ◽  
Günther Redenz ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Elderly Subjects ◽  
Clinical Test ◽  
Clinical Tests ◽  
Timed Up And Go ◽  
Institutionalized Elderly ◽  
Walking Aids ◽  
Measurement Units

Abstract Study aim: This study aims to identify biomechanical gait variables explaining clinical test results in institutionalized elderly people. Material and methods: Twenty-nine elderly (82.0 ± 6.3 years) residents in a nursing home were assessed. They were able to walk 10 meters without walking aids. First, the spontaneous gait was assessed using inertial measurement units in a 10-meter long corridor. Fifteen biomechanical gait variables were analyzed. Then, three clinical tests usually used in elderly subjects were applied: the Timed Up and Go (TUG) test, the Tinetti Scale and the Sit to Stand (STS) test. A correlation matrix using Pearson’s correlation coefficient between clinical and biomechanical variables was performed, obtaining a total of 45 potential correlations. A stepwise multiple linear regression analysis was then performed to determine the influence of each variable. Results: TUG, Tinetti and STS were significantly correlated with similar biomechanical variables, including temporal, temporo-spatial and kinematic variables. Adults over 80 years old and women showed stronger correlations. Single support and ankle angle at takeoff were the two most important variables in stepwise regression analysis. Conclusions: In institutionalized elderly subjects, clinical variables for gait and postural stability are correlated with the biomechanical gait variables, especially in women and adults aged over 80 years.

scholarly journals Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 54
Author(s):  
Barry R. Greene ◽  
Isabella Premoli ◽  
Killian McManus ◽  
Denise McGrath ◽  
Brian Caulfield
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Strong Association ◽  
Sensor Data ◽  
Clinical Population ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Serious Injury

People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.

scholarly journals Minimal shoes improve stability and mobility in persons with a history of falls

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tomasz Cudejko ◽  
James Gardiner ◽  
Asangaedem Akpan ◽  
Kristiaan D’Août
Keyword(s):  
Fall Prevention ◽  
Postural Stability ◽  
Center Of Pressure ◽  
Stability Margin ◽  
Timed Up And Go ◽  
Balance Test ◽  
Margin Of Stability ◽  
Eyes Open ◽  
History Of ◽  
Falls In Older Adults

AbstractPostural and walking instabilities contribute to falls in older adults. Given that shoes affect human locomotor stability and that visual, cognitive and somatosensory systems deteriorate during aging, we aimed to: (1) compare the effects of footwear type on stability and mobility in persons with a history of falls, and (2) determine whether the effect of footwear type on stability is altered by the absence of visual input or by an additional cognitive load. Thirty participants performed standing and walking trials in three footwear conditions, i.e. conventional shoes, minimal shoes, and barefoot. The outcomes were: (1) postural stability (movement of the center of pressure during eyes open/closed), (2) walking stability (Margin of Stability during normal/dual-task walking), (3) mobility (the Timed Up and Go test and the Star Excursion Balance test), and (4) perceptions of the shoes (Monitor Orthopaedic Shoes questionnaire). Participants were more stable during standing and walking in minimal shoes than in conventional shoes, independent of visual or walking condition. Minimal shoes were more beneficial for mobility than conventional shoes and barefoot. This study supports the need for longitudinal studies investigating whether minimal footwear is more beneficial for fall prevention in older people than conventional footwear.

scholarly journals Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lisha Yu ◽  
Yang Zhao ◽  
Hailiang Wang ◽  
Tien-Lung Sun ◽  
Terrence E. Murphy ◽  
...  
Keyword(s):  
Predictive Models ◽  
Cross Validation ◽  
Motor Coordination ◽  
Predictive Accuracy ◽  
Short Form ◽  
Community Dwelling ◽  
Regularized Regression ◽  
Balance Assessment ◽  
Timed Up And Go ◽  
Functional Balance

Abstract Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

scholarly journals On Expressive Features for Gait Analysis using Lower Limb Inertial Sensor Data

2020 ◽  
Vol 53 (2) ◽  
pp. 15990-15997
Author(s):  
Felix Laufer ◽  
Michael Lorenz ◽  
Bertram Taetz ◽  
Gabriele Bleser
Keyword(s):  
Gait Analysis ◽  
Lower Limb ◽  
Inertial Sensor ◽  
Sensor Data

scholarly journals Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew P. Creagh ◽  
Florian Lipsmeier ◽  
Michael Lindemann ◽  
Maarten De Vos
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Heterogeneous Data ◽  
Fine Tuning ◽  
Sensor Data ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Healthcare Applications ◽  
Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

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